Use of pharmacogenetics for predicting cancer prognosis and treatment exposure, response and toxicity

Precision oncology: current and future platforms for treatment selection

Genomic profiling of hundreds of cancer-associated genes is now a component of routine cancer care. DNA sequencing can identify mutations, mutational signatures, and structural alterations predictive of therapy response and assess for heritable cancer risk, but it has been less useful for identifying predictive biomarkers of sensitivity to cytotoxic chemotherapies, antibody drug conjugates, and immunotherapies. The clinical adoption of molecular profiling platforms such as RNA sequencing better suited to identifying those patients most likely to respond to immunotherapies and drug combinations will be critical to expanding the benefits of precision oncology. This review discusses the potential advantages of innovative molecular and functional profiling platforms designed to replace or complement targeted DNA sequencing and the major hurdles to their clinical adoption.

Pharmacogenetics of Breast Cancer Treatments: A Sub-Saharan Africa Perspective

Breast cancer is the most frequent cause of cancer death in low- and middle-income countries, in particular among sub-Saharan African women, where response to available anticancer treatment therapy is often limited by the recurrent breast tumours and metastasis, ultimately resulting in decreased overall survival rate. This can also be attributed to African genomes that contain more variation than those from other parts of the world. The purpose of this review is to summarize published evidence on pharmacogenetic and pharmacokinetic aspects related to specific available treatments and the known genetic variabilities associated with metabolism and/or transport of breast cancer drugs, and treatment outcomes when possible. The emphasis is on the African genetic variation and focuses on the genes with the highest strength of evidence, with a close look on CYP2A6, CYP2B6, CYP2C8, CYP2C9, CYP2C19, CYP2D6, CYP3A4/5, CYP19A1, UGT1A4, UGT2B7, UGT2B15, SLC22A16, SLC38A7, FcγR, DPYD, ABCB1, and SULT1A1, which are the genes known to play major roles in the metabolism and/or elimination of the respective anti-breast cancer drugs given to the patients. The genetic variability of their metabolism could be associated with different metabolic phenotypes that may cause reduced patients' adherence because of toxicity or sub-therapeutic doses. Finally, this knowledge enhances possible personalized treatment approaches, with the possibility of improving survival outcomes in patients with breast cancer.

Adjuvant breast cancer treatments cardiotoxicity and modern methods of detection and prevention of cardiac complications

The most common cancer diagnosis in female population is breast cancer, which affects every year about 2.0 million women worldwide. In recent years, significant progress has been made in oncological therapy, in systemic treatment, and in radiotherapy of breast cancer. Unfortunately, the improvement in the effectiveness of oncological treatment and prolonging patients' life span is associated with more frequent occurrence of organ complications, which are side effects of this treatment. Current recommendations suggest a periodic monitoring of the cardiovascular system in course of oncological treatment. The monitoring includes the assessment of occurrence of risk factors for cardiovascular diseases in combination with the evaluation of the left ventricular systolic function using echocardiography and electrocardiography as well as with the analysis of the concentration of cardiac biomarkers. The aim of this review was critical assessment of the breast cancer therapy cardiotoxicity and the analysis of methods its detections. The new cardio-specific biomarkers in serum, the development of modern imaging techniques (Global Longitudinal Strain and Three-Dimensional Left Ventricular Ejection Fraction) and genotyping, and especially their combined use, may become a useful tool for identifying patients at risk of developing cardiotoxicity, who require further cardiovascular monitoring or cardioprotective therapy.

Feasibility of pharmacometabolomics to identify potential predictors of paclitaxel pharmacokinetic variability

PURPOSE

Paclitaxel is a commonly used chemotherapy drug with substantial variability in pharmacokinetics (PK) that affects treatment efficacy and toxicity. Pharmacometabolomic signatures that explain PK variability could be used to individualize dosing to improve therapeutic outcomes. The objective of this study was to identify pretreatment metabolites or metabolomic signatures that explain variability in paclitaxel PK.

METHODS

This analysis was conducted using data previously collected on a prospective observational study of 48 patients with breast cancer receiving weekly 80 mg/m² paclitaxel infusions. Paclitaxel plasma concentrations were measured during the first infusion to estimate paclitaxel time above threshold (T_c>0.05) and maximum concentration (C_max). Metabolites measured in pretreatment whole blood by nuclear magnetic resonance spectrometry were analyzed for an association with T_c>0.05 and C_max using Pearson correlation followed by stepwise linear regression.

RESULTS

Pretreatment creatinine, glucose, and lysine concentrations were positively correlated with T_c>0.05, while pretreatment betaine was negatively correlated and lactate was positively correlated with C_max (all uncorrected p < 0.05). After stepwise elimination, creatinine was associated with T_c>0.05, while betaine and lactate were associated with C_max (all p < 0.05).

CONCLUSION

This study identified pretreatment metabolites that may be associated with paclitaxel PK variability demonstrating feasibility of a pharmacometabolomics approach for understanding paclitaxel PK. However, identification of more robust pharmacometabolomic predictors will be required for broad and routine application for the clinical dosing of paclitaxel.

The association between adverse events and outcome under checkpoint inhibitors: Where is the deal?

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A review which lays out different potential contributions which can help to understand the IRAEs-outcome link.

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There is a possibility to compute a multifactorial index to characterise patients as ICI sensitive or ICI unsensitive.

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Prospective trails with ICIs are now fesaible to shape patient care beyond high -dose steroids.

Recent reports have put into evidence the possibility of a link between immune-related adverse events (IRAEs) and treatment outcome, patients drawing a benefit from treatment being also exposed to the risk to develop toxicity.

A still unanswered question remains the biological origin(s) which can sustain and explain such a relationship.

The purpose of this review paper is to lay out different potential contributions which can help to understand the IRAEs-outcome link and to propose clinical perspectives taking advantage of this association.

In this respect, pharmacokinetics aspects, immunological and immunogenetics implications have been taken into consideration.

Pharmacogenomics to Predict Tumor Therapy Response: A Focus on ATP-Binding Cassette Transporters and Cytochromes P450

Pharmacogenomics is an evolving tool of precision medicine. Recently, due to the introduction of next-generation sequencing and projects generating "Big Data", a plethora of new genetic variants in pharmacogenes have been discovered. Cancer resistance is a major complication often preventing successful anticancer treatments. Pharmacogenomics of both somatic mutations in tumor cells and germline variants may help optimize targeted treatments and improve the response to conventional oncological therapy. In addition, integrative approaches combining copy number variations and long noncoding RNA profiling with germline and somatic variations seem to be a promising approach as well. In pharmacology, expression and enzyme activity are traditionally the more studied aspects of ATP-binding cassette transporters and cytochromes P450. In this review, we briefly introduce the field of pharmacogenomics and the advancements driven by next-generation sequencing and outline the possible roles of genetic variation in the two large pharmacogene superfamilies. Although the evidence needs further substantiation, somatic and copy number variants as well as rare variants and common polymorphisms in these genes could all affect response to cancer therapy. Regulation by long noncoding RNAs has also been shown to play a role. However, in all these areas, more comprehensive studies on larger sets of patients are needed.

Pharmacogenetics in the treatment of gastrointestinal stromal tumors - an updated review

INTRODUCTION

Gastrointestinal stromal tumors (GIST) are the best example of a targeted therapy in solid tumors. The introduction of tyrosine kinase inhibitors (TKIs) deeply improved the prognosis of this tumor. However, a degree of inter-patient variability is still reported in response rates and pharmacogenetics may play an important role in the final clinical outcome.

AREAS COVERED

In this review, the authors provide an updated overview of the pharmacogenetic literature analyzing the role of polymorphisms in both GIST treatment efficacy and toxicity.

EXPERT OPINION

Besides the primary role of somatic DNA in dictating the clinical response to TKIs, several polymorphisms influencing their pharmacokinetics and pharmacodynamics have been identified as being potentially involved. In the last 10 years, many potential biomarkers have been proposed to predict clinical response and toxicity after TKI administration. However, the evidence is still too limited to promote a clinical translation. To date, the somatic mutational status represents the main player in clinical response to TKIs in GIST treatment; however, pharmacogenetics could still explain the degree of inter-patient variability observed in GIST patients. A combination of different theoretical approaches, experimental model systems, and statistical methods is clearly needed, in order to translate pharmacogenetics to clinical practice in the near future.

Pharmacogenomic potential in advanced cancer patients

PURPOSE

The prevalence of pharmacogenetically actionable medications in advanced cancer patients whose therapy may be optimized with genotype data was determined.

METHODS

Patients enrolled in our institutional molecular tumor board observational cohort were included in this study. Collected data included demographics, type(s) of cancer, and outpatient medications. Medications were classified as "pharmacogenetically actionable" if there are Clinical Pharmacogenetics Implementation Consortium (CPIC) therapeutic recommendations for that medication based on the presence of germline variations. The prevalence of pharmacogenetically actionable medications in the study population was determined, and the frequency of opportunities for pharmacogenetic prescribing and adverse event (AE) mitigation were estimated.

RESULTS

In a cohort of 193 patients with advanced cancer, 65% of patients were taking a pharmacogenetically actionable medication. Approximately 10% of the outpatient medications taken by the study population had a pharmacogenetic association. The most common pharmacogenetically actionable medications being used were ondansetron (47%), capecitabine (10%), and sertraline (7%). Using published genetic variation frequencies and AE risk, we conservatively estimated that 7.1% of cancer patients would be eligible for genetic-based medication adjustment, and 101 AEs would be prevented in 10,000 patients genotyped.

CONCLUSION

Medications with pharmacogenetic associations are used commonly in the advanced cancer patient population. This widespread exposure supports the implementation of prospective genotyping in the treatment of these high-risk patients.

Setting the Dose of Checkpoint Inhibitors: The Role of Clinical Pharmacology

Cancer immunotherapy is based on checkpoint inhibitors (CPIs) that significantly improve the clinical outcome of several malignant diseases. These inhibitors are monoclonal antibodies (mAbs) directed at cytotoxic T lymphocyte-associated protein 4 (CTLA-4), programmed cell death 1 (PD-1), or programmed death-ligand 1 (PD-L1), sharing most of the clinical pharmacokinetic characteristics of mAb targeted therapies, all of which differ from those of cytotoxics and small molecules. Establishing the labeled dose of mAbs, and particularly of the CPIs, represents a true challenge. This review therefore examines the main criteria used for dose selection, along with their limits. The relationships between CPI pharmacokinetic parameters and treatment outcome (efficacy and/or toxicity) differ somewhat among the various drugs, but general features can be identified. Nevertheless, the interpretation of these relationships remains quite controversial. A first interpretation asserts that inter-individual pharmacokinetic variability in clearance has an impact on outcome and should be taken into consideration for dosing individualization. The second considers that higher clearance values observed in some patients result from characteristics associated with poor predictive factors of efficacy. Finally, the schedule, and particularly its frequency of administration, merits rethinking.

Germinal Immunogenetics predict treatment outcome for PD-1/PD-L1 checkpoint inhibitors

Background Checkpoint inhibitors bring marked benefits but only in a minority of patients and may also be associated with severe adverse events. Treatment outcome still cannot be faithfully predicted. The following study hypothesized that host genetics could be applied as predictive biomarkers for checkpoint inhibitor response and immune-related adverse events. We conducted a study based on germinal polymorphisms from genes coding for proteins involved in immune regulation. Methods Germinal DNA was obtained from advanced cancer patients treated with anti-PD-1/PD-L1 checkpoint inhibitors. DNA was genotyped using a custom panel of 166 single nucleotide polymorphisms covering 86 preselected immunogenetic-related genes. Computational analysis using a GTEX portal was made to determine potential expression Quantitative Trait Loci in tissues. Results Ninety-four consecutive patients were included. Objective response rate (complete or partial response) was significantly correlated to tumor microenvironment-related SNPs concerning CCL2, NOS3, IL1RN, IL12B, CXCR3 and IL6R genes. Toxicity were linked to target-related gene SNPs including UNG, IFNW1, CTLA4, PD-L1 and IFNL4 genes. The Area Under the ROC curve (AUC) was 0.81 (95% CI: 0.72-0.9) for response and 0.89 (95% CI: 0.76-1.00) for toxicity. In silico functionality exploring pointed rs4845618 (IL6R), rs10964859 (IFNW1) and rs3087243 (CTLA4) as potentially impacting gene expression. Conclusion These results strongly support a role for distinct immunogenetic-related gene SNPs able to predict efficacy and safety of anti-PD1/PD-L1 therapies. The results highlight the existence of patient-specific, germinal biomarkers able predict response to checkpoint inhibitor efficacy and, possibly, to predict treatment-related adverse events.

Developing a Multi-Dose Computational Model for Drug-Induced Hepatotoxicity Prediction Based on Toxicogenomics Data

Drug-induced hepatotoxicity may cause acute and chronic liver disease, leading to great concern for patient safety. It is also one of the main reasons for drug withdrawal from the market. Toxicogenomics data has been widely used in hepatotoxicity prediction. In our study, we proposed a multi-dose computational model to predict the drug-induced hepatotoxicity based on gene expression and toxicity data. The dose/concentration information after drug treatment is fully utilized in our study based on the dose-response curve, thus a more informative representative of the dose-response relationship is considered. We also proposed a new feature selection method, named MEMO, which is also one important aspect of our multi-dose model in our study, to deal with the high-dimensional toxicogenomics data. We validated the proposed model using the TG-GATEs, which is a large database recording toxicogenomics data from multiple views. The experimental results show that the drug-induced hepatotoxicity can be predicted with high accuracy and efficiency using the proposed predictive model.

Germinal immunogenetics as a predictive factor for immunotherapy

Clinical response to checkpoint inhibitors-based (CPIs) therapies can vary among tumor types and between patients. This led to a significant amount of pre-clinical and clinical research into biomarker identification. Biomarkers have been found to cover both the tumor itself and the tumor microenvironment. Entering host-related parameters into the equation should provide a valuable strategy for identifying not only factors predictive of treatment efficacy but also of treatment-related toxicity. It is clear that germline variants can offer efficient and easily-assessable indicators (blood DNA) to enlarge the spectrum of predictive markers for CPI-based treatment. A major issue concerns the real functional significance of the reported single-nucleotide polymorphisms (SNPs) linked to CPI-treatment outcome. Powered calculations should lead to an optimal trade-off between sample size and allele frequency. New molecular technologies and new analytical methods should provide opportunities to bridge the knowledge gap between SNP-CPI treatment associations and the functional impact of these SNPs.

Clinical Pharmacogenetics for Precision Medicine: Successes and Setbacks

BACKGROUND

Pharmacogenetics is a key component in the delivery of therapeutics to maximize pharmacologic efficacy and minimize toxicity. There are numerous identified gene-drug pairs that demonstrate the utility of pharmacogenetics testing for drug or dose selection. Although some of these pairs have translated into clinical use, pharmacogenetic testing has not yet made its way into routine clinical practice at many institutions.

CONTENT

This review provides an overview of clinically actionable pharmacogenetics in precision medicine. Examples of successfully implemented gene-drug pairs, along with common testing methodologies and guidelines for application, are discussed. Remaining barriers to widespread clinical implementation are also examined.

SUMMARY

There is a recognized role for genotyping in the guidance of therapeutic drug regimens and the prevention of adverse drug reactions. Evidence-based guidelines are available to aid in the selection of treatment upon pharmacogenetics testing for established gene-drug pairs. Multidisciplinary clinical collaboration and clinical decision support tools will be critical for widespread adoption, and financial reimbursement barriers remain.

Evaluation of person-level heterogeneity of treatment effects in published multiperson N-of-1 studies: systematic review and reanalysis

OBJECTIVE

Individual patients with the same condition may respond differently to similar treatments. Our aim is to summarise the reporting of person-level heterogeneity of treatment effects (HTE) in multiperson N-of-1 studies and to examine the evidence for person-level HTE through reanalysis.

STUDY DESIGN

Systematic review and reanalysis of multiperson N-of-1 studies.

DATA SOURCES

Medline, Cochrane Controlled Trials, EMBASE, Web of Science and review of references through August 2017 for N-of-1 studies published in English.

STUDY SELECTION

N-of-1 studies of pharmacological interventions with at least two subjects.

DATA SYNTHESIS

Citation screening and data extractions were performed in duplicate. We performed statistical reanalysis testing for person-level HTE on all studies presenting person-level data.

RESULTS

We identified 62 multiperson N-of-1 studies with at least two subjects. Statistical tests examining HTE were described in only 13 (21%), of which only two (3%) tested person-level HTE. Only 25 studies (40%) provided person-level data sufficient to reanalyse person-level HTE. Reanalysis using a fixed effect linear model identified statistically significant person-level HTE in 8 of the 13 studies (62%) reporting person-level treatment effects and in 8 of the 14 studies (57%) reporting person-level outcomes.

CONCLUSIONS

Our analysis suggests that person-level HTE is common and often substantial. Reviewed studies had incomplete information on person-level treatment effects and their variation. Improved assessment and reporting of person-level treatment effects in multiperson N-of-1 studies are needed.

Imatinib-induced ophthalmological side-effects in GIST patients are associated with the variations of EGFR, SLC22A1, SLC22A5 and ABCB1

Imatinib-induced ophthalmological side-effects, including conjunctiva hemorrhage and periorbital oedema, although very common and still remain relatively little understood. The present study investigated the effects of genetic polymorphisms of drug targets and membrane transporters on these side effects. We found that the minor allele of EGFR rs10258429 and SLC22A1 rs683369 were significant risk determinants of conjunctival hemorrhage with OR of 7.061 (95%CI=1.791-27.837, P=0.005 for EGFR rs10258429 CT+TT vs CC), and 4.809 (95%CI=1.267-18.431, P=0.021 for SLC22A1 rs683369 GG+CG vs CC). The minor allele of SLC22A5 rs274558 and ABCB1 rs2235040 were protective factors to periorbital oedema with OR of 0.313 (95%CI=0.149-0.656, P=0.002 for SLC22A5 rs274558 AA+AG vs GG), and 0.253 (95%CI=0.079-0.805, P=0.020 for ABCB1 rs2235040 CT vs CC). These results indicated that variants in EGFR, SLC22A1, SLC22A5 and ABCB1 influenced the incidence of Imatinib-induced ophthalmological toxicities, and polymorphism analyses in associated genes might be beneficial to optimize Imatinib treatment.

State of Art of Cancer Pharmacogenomics in Latin American Populations

Over the past decades, several studies have shown that tumor-related somatic and germline alterations predicts tumor prognosis, drug response and toxicity. Latin American populations present a vast geno-phenotypic diversity due to the great interethnic and interracial mixing. This genetic flow leads to the appearance of complex characteristics that allow individuals to adapt to endemic environments, such as high altitude or extreme tropical weather. These genetic changes, most of them subtle and unexplored, could establish a mutational profile to develop new pharmacogenomic therapies specific for Latin American populations. In this review, we present the current status of research on somatic and germline alterations in Latin America compared to those found in Caucasian and Asian populations.

Pharmacogenetics Biomarkers and Their Specific Role in Neoadjuvant Chemoradiotherapy Treatments: An Exploratory Study on Rectal Cancer Patients

Background: Pathological complete response (pCR) to neoadjuvant chemoradiotherapy (CRT) in locally advanced rectal cancer (LARC) is still ascribed to a minority of patients. A pathway based-approach could highlight the predictive role of germline single nucleotide polymorphisms (SNPs). The primary aim of this study was to define new predictive biomarkers considering treatment specificities. Secondary aim was to determine new potential predictive biomarkers independent from radiotherapy (RT) dosage and cotreatment with oxaliplatin. Methods: Thirty germ-line SNPs in twenty-one genes were selected according to a pathway-based approach. Genetic analyses were performed on 280 LARC patients who underwent fluoropyrimidine-based CRT. The potential predictive role of these SNPs in determining pathological tumor response was tested in Group 1 (94 patients undergoing also oxaliplatin), Group 2 (73 patients treated with high RT dosage), Group 3 (113 patients treated with standard RT dosage), and in the pooled population (280 patients). Results: Nine new predictive biomarkers were identified in the three groups. The most promising one was rs3136228-MSH6 (p = 0.004) arising from Group 3. In the pooled population, rs1801133-MTHFR showed only a trend (p = 0.073). Conclusion: This exploratory study highlighted new potential predictive biomarkers of neoadjuvant CRT and underlined the importance to strictly define treatment peculiarities in pharmacogenetic analyses.

Validating the pharmacogenomics of chemotherapy-induced cardiotoxicity: What is missing?

The cardiotoxicity of certain chemotherapeutic agents is now well-established, and has led to the development of the field of cardio-oncology, increased cardiac screening of cancer patients, and limitation of patients' maximum cumulative chemotherapeutic dose. The effect of chemotherapeutic regimes on the heart largely involves cardiomyocyte death, leading to cardiomyopathy and heart failure, or the induction of arrhythmias. Of these cardiotoxic drugs, those resulting in clinical cardiotoxicity can range from 8 to 26% for doxorubicin, 7-28% for trastuzumab, or 5-30% for paclitaxel. For tyrosine kinase inhibitors, QT prolongation and arrhythmia, ischemia and hypertension have been reported in 2-35% of patients. Furthermore, newly introduced chemotherapeutic agents are commonly used as part of changed combinational regimens with significantly increased incidence of cardiotoxicity. It is widely believed that the mechanism of action of these drugs is often independent of their cardiotoxicity, and the basis for why these drugs specifically affect the heart has yet to be established. The genetic rationale for why certain patients experience cardiotoxicity whilst other patients can tolerate high chemotherapy doses has proven highly illusive. This has led to significant genomic efforts using targeted and genome-wide association studies (GWAS) to divine the pharmacogenomic cause of this predilection. With the advent of human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs), the putative risk and protective role of single nucleotide polymorphisms (SNPs) can now be validated in a human model. Here we review the state of the art knowledge of the genetic predilection to chemotherapy-induced cardiotoxicity and discuss the future for establishing and validating the role of the genome in this disease.

Functional Analysis of SNPs in the ERCC5 Promoter in Advanced Colorectal Cancer Patients Treated With Oxaliplatin-Based Chemotherapy

The promoter is the center for regulation of gene transcription due to containing numerous transcription factor binding sites. The aim of the study was to determine whether genetic variations at excision repair cross complementation group 5 (ERCC5) promoter could affect transcription factor binding and whether such single nucleotide polymorphism (SNP)-dependent binding could affect gene expression, drug response, and clinical outcome.A total of 170 patients who were cytologically or histologically confirmed with advanced colorectal cancer (CRC), at least 1 measurable lesion, and underwent oxaliplatin-based chemotherapy were studied. The polymerase chain reaction-ligation detection reaction (PCR-LDR) was used to analyze SNPs. The reporter gene assay system and electrophoretic mobility shift assays (EMSA) were performed to investigate the effect of SNPs on the ERCC5 promoter activity and DNA-binding activity, respectively. The mRNA and protein expression of ERCC5 in tumor tissues of colorectal cancer patients with different genotypes were detected by real-time PCR and western blot, respectively.Both -763A and -763G allele had nuclear protein-binding ability. +25A allele did not show any nuclear protein-binding ability, whereas +25G allele did. The relative luciferase activity of the -763A/+25G haplotype was significantly higher than other 3 haplotypes (P < 0.05). The expression level of ERCC5 mRNA and protein was significantly higher in tumor tissues with -763AA+25GG genotype combination than that with -763GG+25AA genotype combination (P < 0.05, respectively). Allelic variants (-763AA vs -763AG or -763GG, +25GG versus +25AG or +25AA) were significantly associated with shorter progression-free survival (PFS) and overall survival (OS) (P < 0.05, respectively). At multivariate analysis, patients with risk genotypes (-763AA or +25GG genotype) demonstrated a significantly increasing risk of progression (P = 0.01) or worse OS (P = 0.001).The ERCC5 promoter polymorphisms at -763 and +25 may be important functional variants and predictors of clinical outcome of CRC patients who received oxaliplatin chemotherapy.

Evidence for association of SNPs in ABCB1 and CBR3, but not RAC2, NCF4, SLC28A3 or TOP2B, with chronic cardiotoxicity in a cohort of breast cancer patients treated with anthracyclines

AIMS

Validation of associations for SNPs in RAC2, NCF4 and SLC28A3, identification of a novel association with a TOP2B SNP and screening 23 SNPs putatively relevant to anthracycline-induced cardiotoxicity.

PATIENTS & METHODS

A total of 166 breast cancer patients treated with doxorubicin underwent echocardiogram, including 19 cases with systolic dysfunction (ejection fraction <55%) and 147 controls. Four high priority SNPs were tested in the primary analysis, with appropriate statistical correction, and 23 additional SNPs were screened in an uncorrected secondary analysis.

RESULTS

Previously reported associations for RAC2, NCF4 and SLC28A3 could not be validated and a novel association with TOP2B was not discovered in this cohort (all p > 0.05), likely due to inadequate power. Two SNPs were identified in the uncorrected secondary analysis including a protective SNP in ABCB1 (3435C>T, p = 0.049) and a risk allele in CBR3 (V244M, p = 0.012).

CONCLUSION

The associations reported in prior publications and those discovered in this secondary analysis require further replication in independent cohorts.

Enabling personalized cancer medicine decisions: The challenging pharmacological approach of PBPK models for nanomedicine and pharmacogenomics (Review)

The existing tumor heterogeneity and the complexity of cancer cell biology critically demand powerful translational tools with which to support interdisciplinary efforts aiming to advance personalized cancer medicine decisions in drug development and clinical practice. The development of physiologically based pharmacokinetic (PBPK) models to predict the effects of drugs in the body facilitates the clinical translation of genomic knowledge and the implementation of in vivo pharmacology experience with pharmacogenomics. Such a direction unequivocally empowers our capacity to also make personalized drug dosage scheme decisions for drugs, including molecularly targeted agents and innovative nanoformulations, i.e. in establishing pharmacotyping in prescription. In this way, the applicability of PBPK models to guide individualized cancer therapeutic decisions of broad clinical utility in nanomedicine in real-time and in a cost-affordable manner will be discussed. The latter will be presented by emphasizing the need for combined efforts within the scientific borderlines of genomics with nanotechnology to ensure major benefits and productivity for nanomedicine and personalized medicine interventions.

Genotyping concordance in DNA extracted from formalin-fixed paraffin embedded (FFPE) breast tumor and whole blood for pharmacogenetic analyses

BACKGROUND

Cancer pharmacogenetic studies use archival tumor samples as a DNA source when germline DNA is unavailable. Genotyping DNA from formalin-fixed paraffin embedded tumors (FFPE-T) may be inaccurate due to FFPE storage, genetic aberrations, and/or insufficient DNA extraction. Our objective was to assess the extent and source of genotyping inaccuracy from FFPE-T DNA and demonstrate analytical validity of FFPE-T genotyping of candidate single nucleotide polymorphisms (SNPs) for pharmacogenetic analyses.

METHODS

Cancer pharmacogenetics SNPs were genotyped by Sequenom MassARRAYs in DNA harvested from matched FFPE-T, FFPE lymph node (FFPE-LN), and whole blood leukocyte samples obtained from breast cancer patients. No- and discordant-call rates were calculated for each tissue type and SNP. Analytical validity was defined as any SNP with <5% discordance between FFPE-T and blood and <10% discordance plus no-calls.

RESULTS

Matched samples from 114 patients were genotyped for 247 SNPs. No-call rate in FFPE-T was greater than FFPE-LN and blood (4.3% vs. 3.0% vs. 0.5%, p < 0.001). Discordant-call rate between FFPE-T and blood was very low, but greater than that between FFPE-LN and blood (1.1% vs. 0.3%, p < 0.001). Samples with heterozygous genotypes were more likely to be no- or discordantly-called in either tissue (p < 0.001). Analytical validity of FFPE-T genotyping was demonstrated for 218 (88%) SNPs.

CONCLUSIONS

No- and discordant-call rates were below concerning thresholds, confirming that most SNPs can be accurately genotyped from FFPE-T on our Sequenom platform. FFPE-T is a viable DNA source for prospective-retrospective pharmacogenetic analyses of clinical trial cohorts.

Personalized Medicine in Gastrointestinal Stromal Tumor (GIST): Clinical Implications of the Somatic and Germline DNA Analysis

Gastrointestinal stromal tumors (GIST) are the most common mesenchymal tumors of the gastrointestinal tract. They are characterized by gain of function mutations in KIT or PDGFRA tyrosine kinase receptors, with their consequent constitutive activation. The gold standard therapy is imatinib that offers a good and stable response for approximately 18–36 months. However, resistance is very common and it is vital to identify new biomarkers. Up until now, there have been two main approaches with focus to characterize novel targets. On the one hand, the focus is on the tumor genome, as the final clinical outcome depends mainly from the cancer specific mutations/alterations patterns. However, the germline DNA is important as well, and it is inconceivable to think the patients response to the drug is not related to it. Therefore the aim of this review is to outline the state of the art of the personalized medicine in GIST taking into account both the tumor DNA (somatic) and the patient DNA (germline).

Germline and somatic genetic predictors of pathological response in neoadjuvant settings of rectal and esophageal cancers: systematic review and meta-analysis

Oncologists have pointed out an urgent need for biomarkers that can be useful for clinical application to predict the susceptibility of patients to preoperative therapy. This review collects, evaluates and combines data on the influence of reported somatic and germline genetic variations on histological tumor regression in neoadjuvant settings of rectal and esophageal cancers. Five hundred and twenty-seven articles were identified, 204 retrieved and 61 studies included. Among 24 and 14 genetic markers reported for rectal and esophageal cancers, respectively, significant associations in meta-analyses were demonstrated for the following markers. In rectal cancer, major response was more frequent in carriers of the TYMS genotype 2 R/2 R-2 R/3 R (rs34743033), MTHFR genotype 677C/C (rs1801133), wild-type TP53 and KRAS genes. In esophageal cancer, successful therapy appeared to correlate with wild-type TP53. These results may be useful for future research directions to translate reported data into practical clinical use.

Personalized radiotherapy: concepts, biomarkers and trial design

In the past decade, and pointing onwards to the immediate future, clinical radiotherapy has undergone considerable developments, essentially including technological advances to sculpt radiation delivery, the demonstration of the benefit of adding concomitant cytotoxic agents to radiotherapy for a range of tumour types and, intriguingly, the increasing integration of targeted therapeutics for biological optimization of radiation effects. Recent molecular and imaging insights into radiobiology will provide a unique opportunity for rational patient treatment, enabling the parallel design of next-generation trials that formally examine the therapeutic outcome of adding targeted drugs to radiation, together with the critically important assessment of radiation volume and dose-limiting treatment toxicities. In considering the use of systemic agents with presumed radiosensitizing activity, this may also include the identification of molecular, metabolic and imaging markers of treatment response and tolerability, and will need particular attention on patient eligibility. In addition to providing an overview of clinical biomarker studies relevant for personalized radiotherapy, this communication will highlight principles in addressing clinical evaluation of combined-modality-targeted therapeutics and radiation. The increasing number of translational studies that bridge large-scale omics sciences with quality-assured phenomics end points-given the imperative development of open-source data repositories to allow investigators the access to the complex data sets-will enable radiation oncology to continue to position itself with the highest level of evidence within existing clinical practice.

Biomarkers of treatment toxicity in combined-modality cancer therapies with radiation and systemic drugs: study design, multiplex methods, molecular networks

Organ toxicity in cancer therapy is likely caused by an underlying disposition for given pathophysiological mechanisms in the individual patient. Mechanistic data on treatment toxicity at the patient level are scarce; hence, probabilistic and translational linkages among different layers of data information, all the way from cellular targets of the therapeutic exposure to tissues and ultimately the patient’s organ systems, are required. Throughout all of these layers, untoward treatment effects may be viewed as perturbations that propagate within a hierarchically structured network from one functional level to the next, at each level causing disturbances that reach a critical threshold, which ultimately are manifested as clinical adverse reactions. Advances in bioinformatics permit compilation of information across the various levels of data organization, presumably enabling integrated systems biology-based prediction of treatment safety. In view of the complexity of biological responses to cancer therapy, this communication reports on a “top-down” strategy, starting with the systematic assessment of adverse effects within a defined therapeutic context and proceeding to transcriptomic and proteomic analysis of relevant patient tissue samples and computational exploration of the resulting data, with the ultimate aim of utilizing information from functional connectivity networks in evaluation of patient safety in multimodal cancer therapy.

Genetic markers for toxicity of adjuvant oxaliplatin and fluoropyrimidines in the phase III TOSCA trial in high-risk colon cancer patients

We investigated 17 polymorphisms in 11 genes (TS, MTHFR, ERCC1, XRCC1, XRCC3, XPD, GSTT1, GSTP1, GSTM1, ABCC1, ABCC2) for their association with the toxicity of fluoropyrimidines and oxaliplatin in colorectal cancer patients enrolled in a prospective randomized trial of adjuvant chemotherapy. The TOSCA Italian adjuvant trial was conducted in high-risk stage II–III colorectal cancer patients treated with 6 or 3 months of either FOLFOX-4 or XELOX adjuvant chemotherapy. In the concomitant ancillary pharmacogenetic study, the primary endpoint was the association of polymorphisms with grade 3–4 CTCAE toxicity events (grade 2–4 for neurotoxicity). In 517 analyzed patients, grade ≥ 3 neutropenia and grade ≥ 2 neurotoxicity events occurred in 150 (29%) and in 132 patients (24.8%), respectively. Diarrhea grade ≥ 3 events occurred in 34 (6.5%) patients. None of the studied polymorphisms showed clinically relevant association with toxicity. Hopefully, genome-wide association studies will identify new and more promising genetic variants to be tested in future studies.

Risk factors for relapse in childhood acute lymphoblastic leukemia: prediction and prevention

With current treatment regimens, survival rates for acute lymphoblastic leukemia (ALL) have improved dramatically since the 1980s, with current 5-year overall survival rates estimated at greater than 85%. This success was achieved, in part, through the implementation of risk-stratified therapy. Nevertheless, for a subgroup of patients (15-20%) with newly diagnosed ALL who will ultimately relapse, traditional risk assessment remains inadequate. The risk of relapse may be estimated on the basis of diagnostic features or early treatment response findings. Further progress in this field may thus come from refinement of predictive factors for relapse and treatment adaptation and from the identification of biological subsets of ALL patients who could benefit from specific target therapies. This article summarizes the aspects associated with the identification of predictive factors for relapse in childhood ALL and options available for prevention of disease recurrence.

Pharmacogenetics of cancer drugs

The variability in treatment outcomes among patients receiving the same therapy for seemingly similar tumors can be attributed in part to genetics. The tumor's (somatic) genome largely dictates the effectiveness of the therapy, and the patient's (germline) genome influences drug exposure and the patient's sensitivity to toxicity. Many potentially clinically useful associations have been discovered between common germline genetic polymorphisms and outcomes of cancer treatment. This review highlights the germline pharmacogenetic associations that are currently being used to guide cancer treatment decisions, those that are most likely to someday be clinically useful, and associations that are well known but their roles in clinical management are not yet certain. In the future, germline genetic information will likely be available from tumor genetic analyses, creating an efficient opportunity to integrate the two genomes to optimize treatment outcomes for each individual cancer patient.

The genetic variants underlying breast cancer treatment-induced chronic and late toxicities: a systematic review

A systematic review was performed to describe the findings from 19 genetic association studies that have examined the genetic variants underlying four common treatment-induced chronic and late toxicities in breast cancer patients, and to evaluate the quality of reporting. Three out of 5 studies found an association between HER2 lle655Val polymorphisms and trastuzumab-induced cardiotoxicity. Two studies found a positive association between cognitive impairment and the Val allele of the COMT gene and the ε4 allele of the apolipoprotein E gene. Genetic associations were established between fatigue and the G/G genotype of IL6-174 and TNF-308, and the Met allele of the COMT gene in 4 studies. Among studies (N=8) that evaluated the genetic associations underlying peripheral neuropathy, CYP2C8∗3 variant is commonly reported as the associated gene. Most studies failed to conform to the major criteria listed in the STREGA guidelines, with a lack of transparent reporting of methods and results.

Bioinformatic approaches to augment study of epithelial-to-mesenchymal transition in lung cancer

Bioinformatic approaches are intended to provide systems level insight into the complex biological processes that underlie serious diseases such as cancer. In this review we describe current bioinformatic resources, and illustrate how they have been used to study a clinically important example: epithelial-to-mesenchymal transition (EMT) in lung cancer. Lung cancer is the leading cause of cancer-related deaths and is often diagnosed at advanced stages, leading to limited therapeutic success. While EMT is essential during development and wound healing, pathological reactivation of this program by cancer cells contributes to metastasis and drug resistance, both major causes of death from lung cancer. Challenges of studying EMT include its transient nature, its molecular and phenotypic heterogeneity, and the complicated networks of rewired signaling cascades. Given the biology of lung cancer and the role of EMT, it is critical to better align the two in order to advance the impact of precision oncology. This task relies heavily on the application of bioinformatic resources. Besides summarizing recent work in this area, we use four EMT-associated genes, TGF-β (TGFB1), NEDD9/HEF1, β-catenin (CTNNB1) and E-cadherin (CDH1), as exemplars to demonstrate the current capacities and limitations of probing bioinformatic resources to inform hypothesis-driven studies with therapeutic goals.

Pharmacogenomics of human uridine diphospho-glucuronosyltransferases and clinical implications

Glucuronidation by uridine diphospho-glucuronosyltransferase enzymes (UGTs) is a major phase II biotransformation pathway and, complementary to phase I metabolism and membrane transport, one of the most important cellular defense mechanisms responsible for the inactivation of therapeutic drugs, other xenobiotics, and endogenous molecules. Interindividual variability in UGT pathways is significant and may have profound pharmacological and toxicological implications. Several genetic and genomic processes underlie this variability and are discussed in relation to drug metabolism and diseases such as cancer.

Androgen receptor activation in castration-recurrent prostate cancer: the role of Src-family and Ack1 tyrosine kinases

There is growing appreciation that castration-recurrent prostate cancer (CR-CaP) is driven by the continued expression of androgen receptor (AR). AR activation in CR-CaP through various mechanisms, including AR overexpression, expression of AR splice variants or mutants, increased expression of co-regulator proteins, and by post-translational modification, allows for the induction of AR-regulated genes in response to very low levels of tissue-expressed, so-called intracrine androgens, resulting in pathways that mediate CaP proliferation, anti-apoptosis and oncogenic aggressiveness. The current review focuses on the role played by Src-family (SFK) and Ack1 non-receptor tyrosine kinases in activating AR through direct phosphorylation, respectively, on tyrosines 534 or 267, and how these modifications facilitate progression to CR-CaP. The fact that SFK and Ack1 are central mediators for multiple growth factor receptor signaling pathways that become activated in CR-CaP, especially in the context of metastatic growth in the bone, has contributed to recent therapeutic trials using SFK/Ack1 inhibitors in monotherapy or in combination with antagonists of the AR activation axis.

Pharmacogenomics in oncology care

Cancer pharmacogenomics have contributed a number of important discoveries to current cancer treatment, changing the paradigm of treatment decisions. Both somatic and germline mutations are utilized to better understand the underlying biology of cancer growth and treatment response. The level of evidence required to fully translate pharmacogenomic discoveries into the clinic has relied heavily on randomized control trials. In this review, the use of observational studies, as well as, the use of adaptive trials and next generation sequencing to develop the required level of evidence for clinical implementation are discussed.